Indoor target positioning method based on improved convolutional neural network model

ABSTRACT

An indoor target positioning method based on an improved convolutional neural network (CNN) model includes acquiring and preprocessing target camera serial interface (CSI) data of a to-be-positioned target and matching the preprocessed target CSI data with fingerprints in a positioning fingerprint database to obtain coordinate information of the to-be-positioned target. The generation method of the positioning fingerprint database includes: collecting indoor WiFi signals by a software defined radio (SDR) platform to obtain indoor CSI data corresponding to the WiFi signals, and preprocessing the indoor CSI data; partitioning the preprocessed indoor CSI data into a plurality of data subsets through a clustering algorithm; training an improved CNN model by the data subsets to obtain a trained improved CNN model; and generating the positioning fingerprint database by the trained improved CNN model and the preprocessed indoor CSI data.

CROSS-REFERENCE TO RELAYED APPLICATIONS

This application is a continuation-in-part of International PatentApplication No. PCT/CN2021/123586 with an international filing date ofOct. 13, 2021, designating the United States, now pending, and furtherclaims foreign priority benefits to Chinese Patent Application No.202110556627.5 filed May 21, 2021. The contents of all of theaforementioned applications, including any intervening amendmentsthereto, are incorporated herein by reference. Inquiries from the publicto applicants or assignees concerning this document or the relatedapplications should be directed to: Matthias Scholl P. C., Attn.: Dr.Matthias Scholl Esq., 245 First Street, 18th Floor, Cambridge, Mass.02142.

BACKGROUND

The disclosure relates to the field of indoor positioning, and moreparticularly to an indoor target positioning method based on an improvedconvolutional neural network (CNN) model.

With the development of urbanization, indoor positioning technology hasbeen widely concerned by people. Global Navigation Satellite System(GNSS) and other known positioning technologies play an increasinglyimportant role in accurate outdoor positioning of objects. However,limited by the interference of reinforced concrete structures onsignals, the positioning effect of these technologies cannot meet therequirements for indoor object positioning. In addition, owing to thelarge area of some indoor places, complex spatial layout structure, andcross-distribution of passages and corridors, global position system(GPS) technology can only receive satellite signals outdoors, so it isdifficult for GPS technology and other positioning technologies torealize indoor positioning quickly.

With the massive use of wireless fidelity (Wi-Fi) signals in largeindoor environments, Wi-Fi signals have inherent advantages in indoorpositioning. However, if the signal is blocked by obstacles in theindoor environment, such as walls, ceilings, closed doors and windows,non-line-of-sight (NLOS) errors will occur (direct light is blocked andonly reflected light is visible). In addition, as a smooth objectsurface will reflect, scatter and diffract direct light in the processof signal propagation, a multi-path phenomenon will occur when areceiver receives all light components generated by reflection,scattering and diffraction of direct light. NLOS errors and multi-pathphenomenon will greatly interfere with the accuracy of indoorpositioning and affect the indoor positioning effect.

SUMMARY

The disclosure provides an indoor target positioning method based on animproved CNN model, the method comprising:

-   -   acquiring and preprocessing target CSI data of a        to-be-positioned target; and    -   matching the preprocessed target CSI data with fingerprints in a        positioning fingerprint database to obtain coordinate        information of the to-be-positioned target.

In a class of this embodiment, the generation method of the positioningfingerprint database comprises:

-   -   collecting indoor WiFi signals by a software defined radio (SDR)        platform to obtain indoor CSI data corresponding to the WiFi        signals, and preprocessing the indoor CSI data;    -   partitioning the preprocessed indoor CSI data into a plurality        of data subsets through a clustering algorithm;    -   training an improved CNN model by the data subsets to obtain a        trained improved CNN model; and    -   generating the positioning fingerprint database by the trained        improved CNN model and the preprocessed indoor CSI data.

In a class of this embodiment, the SDR platform comprises a mobile node,a plurality of signal base stations and a data processing unit; themobile node is configured to periodically broadcast probe request framesand automatically send positioning requests; each signal base station isconfigured to receive the positioning requests of the mobile node andacquire WiFi signals of the mobile node; and the data processing unit isconfigured to process the WiFi signals acquired by the signal basestations to obtain the indoor CSI data corresponding to the WiFisignals.

In a class of this embodiment, preprocessing target camera serialinterface (CSI) data comprises:

-   -   performing amplitude filtering, phase correction, and mean        removal on the indoor CSI data to obtain corrected CSI data; and    -   reducing dimension of and eliminating noise of the corrected CSI        data by principal component analysis (PCA) to obtain the        preprocessed CSI data.

In a class of this embodiment, partitioning the preprocessed indoor CSIdata into a plurality of data subsets through a clustering algorithmcomprises:

dividing the preprocessed indoor CSI data into multiple data subsets byclustering algorithm, and each data subset comprising multiple indoorCSI data;

calculating a center of each data subset based on a k-means algorithm byfollowing formula:

$\begin{matrix}{{\mu_{i} = {\frac{1}{c_{i}}\Sigma_{x \in C_{i}}x}};} & (1)\end{matrix}$

where μ_(i) represents the center of the i^(th) data subset, C_(i)represents the i^(th) data subset, x represents the indoor CSI data,i=1, . . . , k, where k is the number of data subsets;

calculating a square error of all data subsets based on the center ofthe data subset by following formula:

E=Σ _(i=1) ^(k)Σ_(x∈C) _(i) ∥x−μ _(i)∥₂ ²  (2);

where E represents the square error of all data subsets; and

calculating a distance between the preprocessed indoor CSI data and thecenter of each data subset, re-partitioning the data subsets accordingto the distance until the square error is minimized, thereby obtaining afinal data subset.

In a class of this embodiment, the improved CNN model comprises fiveconvolution layers, a first fully connected layer, and a second fullyconnected layer connected in sequence; the five convolution layerscomprise 16, 32, 64, 64 and 128 kernels, respectively; the first fullyconnected layer is configured to flatten outputs of a fifth convolutionlayer, and the second fully connected layer is configured to output thepartitioned CSI data.

In a class of this embodiment, training an improved CNN model by thedata subsets comprises:

standardizing each indoor CSI data in each data subset:

$\begin{matrix}{{x_{p} = \frac{{var}_{p} - {{mean}( {var}_{p} )}}{{total}( {var}_{p} )}};} & (3)\end{matrix}$

where x_(p) represents a p^(th) indoor CSI data after beingstandardized, var_(p) represents a p^(th) indoor CSI data in the datasubset, mean ( ) is a function MEAN, total ( ) is a function SUBTOTAL,p=1, . . . , n, where n is a number of indoor CSI data in each datasubset;

initializing model parameters of the improved CNN model, inputting thestandardized indoor CSI data in each data subset into the convolutionlayers of the improved CNN model to obtain the convoluted indoor CSIdata, and extracting features of the convoluted indoor CSI data;

batching, max-pooling and activating the features of the convolutedindoor CSI data to obtain mappings of the convoluted indoor CSI data;

classifying the mappings of the convoluted indoor CSI data by the secondfully connected layer of the improved CNN model to obtain the coordinateinformation corresponding to each indoor CSI data in each data subset;

calculating losses of the improved CNN model by a loss function based ona default real coordinate label and the coordinate information output bythe improved CNN model; and

updating the model parameters of the improved CNN model based on thelosses, and processing the standardized indoor CSI data by the updatedimproved CNN model until the losses converge, to obtain the trainedimproved CNN model.

In a class of this embodiment, batching the features of each convolutedindoor CSI data comprises:

calculating the mean based on the features of the convoluted indoor CSIdata:

$\begin{matrix}{{U_{B} = {\frac{1}{M}\Sigma_{q = 1}^{M}s_{q}}};} & (4)\end{matrix}$

where U_(B) represents the mean of the features of the convoluted indoorCSI data, M is a number of features of each convoluted indoor CSI data,s_(q) represents the features of the q^(th) convoluted indoor CSI data,q=1, . . . , M;

calculating a variance of the features based on U_(B) by followingformula:

$\begin{matrix}{{\sigma_{B}^{2} = {\frac{1}{M}{\Sigma_{q = 1}^{M}( {s_{q} - U_{B}} )}^{2}}};} & (5)\end{matrix}$

where σ_(B) ² represents the variance of the features of the convolutedindoor CSI data;

standardizing the features of the convoluted indoor CSI data based onU_(B) and σ_(B) ²:

$\begin{matrix}{{= \frac{s_{q} - U_{B}}{\sqrt{\sigma_{B}^{2} + \varepsilon}}};} & (6)\end{matrix}$

where

represents the standardized features of the indoor CSI data, and ε is adefault standardized value.

In a class of this embodiment, the coordinate information correspondingto each indoor CSI data in each data subset is calculated by followingformula:

y _(p)=γ

_(p)+β  (7);

where y_(p) represents the coordinate information corresponding to thep^(th) indoor CSI data in the data subset, γ and β are the modelparameters of the improved CNN model, respectively,

_(p) represents the standardized features of the p^(th) indoor CSI datain the data subset.

In a class of this embodiment, the loss function is expressed asfollows:

L=−Σ _(p) y′ _(p) log(y _(p))  (8);

where L represents the losses of the improved CNN model, y′_(p)represents the real coordinate label of the p^(th) indoor CSI data inthe data subset, y_(p) represents the coordinate informationcorresponding to the p^(th) indoor CSI data in the data subset.

In a class of this embodiment, the positioning fingerprint databasecomprises CSI data and coordinate information corresponding to the CSIdata.

The following advantages are associated with the indoor targetpositioning method based on an improved CNN model of the disclosure.

According to the indoor target positioning method based on an improvedCNN model of the disclosure, the collected CSI data are preprocessed toremove noise interference, and then the indoor CSI data are partitionedinto a plurality of data subsets by a dataset partitioning method, whichis beneficial to improving the performance of position estimation,reducing the computation burden in the position estimation process andfurther improving the accuracy of indoor positioning data. In addition,the indoor CSI data in the data subset are processed by the improved CNNmodel to extract multi-path and non-line-of-sight (NLOS) effects, andthe multi-path and NLOS effects are eliminated even better by a fittingcurve of a neural network to obtain accurate coordinate information forindoor positioning, which effectively improves the accuracy of indoorpositioning.

According to the disclosure, positioning can be realized using Wi-Fisignals covering large indoor places instead of using new positioninginstruments, thus reducing the cost and bringing great convenience.Preprocessing data by the PCA can remove most of the background noiseand reduce the data dimension, so that CSI signals may also be appliedto indoor positioning in multi-path and NLOS environments. Partitioningdata subsets can effectively reduce noise and remove redundancy of eachdata subset, so as to eliminate the final error of each data subset andimprove the positioning accuracy. The improved CNN model is adopted tolearn the features of input data, which solves the problem of highcomputation burden of multidimensional data, and also accuratelyextracts the coordinate information.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of an indoor target positioning method based on animproved CNN model according to the disclosure;

FIG. 2 is a structural diagram of an improved CNN model according to anembodiment of the disclosure; and

FIG. 3 is a flowchart of a fingerprint matching step according to theembodiment of the disclosure.

DETAILED DESCRIPTION

To further illustrate the disclosure, embodiments detailing an indoortarget positioning method based on an improved CNN model are describedbelow. It should be noted that the following embodiments are intended todescribe and not to limit the disclosure.

According to the disclosure, an indoor target positioning method basedon an improved CNN model is proposed. As shown in FIG. 1 , the methodcan be used in an offline stage and an online stage; the model istrained and a fingerprint database is generated in the offline stage,while the real-time indoor target positioning is realized in the onlinestage by the fingerprint database generated in the offline stage.

According to the disclosure, the method comprises the following steps 1and 2.

Step 1, the target CSI data of a to-be-positioned target are acquiredand preprocessed; and

Step 2, the preprocessed target CSI data are matched with fingerprintsin a positioning fingerprint database to obtain coordinate informationof the to-be-positioned target.

The generation method of the positioning fingerprint database comprisesthe following steps A to D.

In step A, indoor WiFi signals are collected by an SDR platform toobtain indoor CSI data corresponding to the WiFi signals, and the dataare preprocessed.

In step B, the preprocessed indoor CSI data are partitioned into aplurality of data subsets through a clustering algorithm.

In step C, the improved CNN model is trained by the data subsets toobtain the trained improved CNN model.

In step D, a positioning fingerprint database is generated by thetrained improved CNN model and the preprocessed indoor CSI data.

According to the disclosure, the SDR platform is configured to collectdata, which can collect WiFi signals and obtain CSI data by the WiFisignals. The CSI data contains more position information than radiofrequency identification devices (RFID) and received signal strengthindicators (RSSI), which makes it easier to extract multi-pathcomponents and is conducive to improving the accuracy of indoorpositioning. The SDR platform comprises a mobile node, a plurality ofsignal base stations and a data processing unit. Both the mobile nodeand the signal base stations are defined by an SDR device, and themobile node can periodically broadcast probe request frames andautomatically send positioning requests. Each signal base station isconfigured to receive the positioning requests from the mobile node andacquire the WiFi signals of the mobile node. A special baseband receiveris disposed behind an RF front end in the base station to reduce thecarrier frequency offset and improve the received signal to noise ratio.The data processing unit is connected to the signal base station througha coaxial cable, and is configured to process the WiFi signals acquiredby the signal base stations to obtain the indoor CSI data correspondingto the WiFi signals. When the mobile node sends the positioningrequests, the probe request frame, which belongs to a management frameand strictly complies with an ieee802.11b protocol, is taken as arequest signal.

In step 1 and the step A, the data may be preprocessed through principalcomponent analysis (PCA) in which a set of linearly irrelevant variablescan be extracted from vast amounts of CSI data, thus solving thecomplexity and data transmission problems associated with super-highdimensional CSI data in complex environments. The data preprocessingcomprises following steps (1) to (4).

In step (1), amplitude filtering, phase correction and mean removal areperformed on the CSI data to obtain corrected CSI data, and the mean ofthe CSI data after mean removal is 0 in each feature dimension.

In step (2), dimension reduction and noise elimination are performed onthe corrected CSI data through PCA. PCA essentially takes the directionwith the largest variance as a main feature, and “dissociates” the datain each orthogonal direction, i.e., makes them irrelevant in differentdirections, which minimizes the impact of noise on experimental data.

The variance of the corrected CSI data is calculated through PCA by thefollowing formula:

$\begin{matrix}{{{{Var}(x)} = {\frac{1}{m}\Sigma_{j = 1}^{m}x_{j}^{2}}};} & (9)\end{matrix}$

where Var(x) represents the variance of the corrected CSI data, m is thenumber of corrected CSI data, x_(j) ² represents the square of the valueof the j^(th) corrected CSI data, j=1, . . . , m.

In step (3), a CSI feature vector is calculated by the variance of thecorrected CSI data; each CSI data contains values in three dimensions(x, y and z axes in a three-dimensional space coordinate system), andmapping directions of the values in three dimensions in thethree-dimensional space constitute a mapping direction w, where w=(w₁,w₂, w₃), and w₁, w₂ and w₃ of the CSI data represent the mappingdirections of the values in three dimensions in x, y and z axes,respectively. According the disclosure, a maximum CSI feature vector isobtained after being mapped to the direction w by gradient boosting:

$\begin{matrix}{{{\nabla f} = {\frac{2}{m}{X^{T}( {Xw} )}}};} & (10)\end{matrix}$

where ∇f represents the variation of gradient, and X represents the CSIfeature vector.

In step (4), the maximum variance of the corrected CSI data is obtainedby formula (10), and the corrected CSI data are processed based on themaximum variance to obtain the CSI data after dimension reduction andnoise elimination, i.e., the preprocessed CSI data.

To improve the learning performance of position estimation, a datasetpartitioning method is proposed in the disclosure, which partitions afingerprint database set of each small indoor space into severalsubsets, and a center of each data subset may be obtained based on ak-means clustering algorithm. Through PCA, the noise can be effectivelyreduced, and the redundancy of each data subset can be removed, so as toextract multi-path and non-line-of-sight (NLOS) effects of each datasubset, which can improve the learning performance of positionestimation, eliminate the final error and improve the positioningaccuracy.

In the embodiment of the disclosure, the step B comprises followingsubsteps B01 to B04.

In step B01, the preprocessed indoor CSI data are randomly partitionedinto a plurality of data subsets based on the geographical position, andeach data subset comprises a plurality of indoor CSI data. For largeindoor places, the data subsets may be randomly partitioned according tothe layout of shops and floors in the indoor places, or based on adefault area (for example, 50 m²).

In step B02, the data subsets are taken as clusters in the clusteringalgorithm, the center of each data subset is calculated based on thek-means algorithm by following specific formula:

$\begin{matrix}{{\mu_{i} = {\frac{1}{C_{i}}\Sigma_{x \in C_{i}}x}};} & (1)\end{matrix}$

where μ_(i) represents the center of the i^(th) data subset (i.e., amean component of the cluster, also known as the center of mass), C_(i)represents the i^(th) data subset, x represents the indoor CSI data,i=1, . . . , k, where k is the number of data subsets.

In step B03, in order to make points in the cluster closely connectedtogether and make the distance between the clusters as large aspossible, in the disclosure, a square error of all data subsets iscalculated based on the center of the data subsets, and iteration isperformed until the square error is minimized. The square error iscalculated by the following formula:

E=Σ _(i=1) ^(k)Σ_(x∈C) _(i) ∥x−μ _(i)∥₂ ²  (2);

where E represents the square error of all data subsets.

In step B04, a distance between the preprocessed indoor CSI data and thecenter of each data subset is calculated, and the data subsets arere-partitioned based on the distance, the steps B02 to B03 are repeateduntil the square error is minimized to obtain a final data subset.

The improved CNN model is adopted in step C of the embodiment of thedisclosure. As shown in FIG. 2 , the improved CNN model comprises fiveconvolution layers and two fully connected layers connected in sequence,where the five convolution layers comprise 16, 32, 64, 64 and 128kernels, respectively. In the first convolution layer, a convolutionoperation is performed to track results in the 16 kernels and extractinformation from the results, and then perform batch normalization,max-pooling and activation. In the second convolution layer, the 32kernels are configured to track results and extract information, andeach kernel is 16 in height, which is equivalent to the number ofkernels in the first convolution layer. The data processing step in thethird, fourth and fifth convolution layers is the same as that in thesecond convolution layer. The first fully connected layer is configuredto flatten outputs of the fifth convolution layer. The second fullyconnected layer is configured to output the partitioned CSI data. Themulti-path and NLOS effects are partitioned into five windows in thedisclosure: (1) a monitoring window without NLOS and multi-path effects;(2) a monitoring window in which NLOS effects appear; (3) a monitoringwindow in which NLOS effects disappear; (4) a monitoring window in whichmulti-path effects appear; and (5) a monitoring window in whichmulti-path effects disappear, and five neural nodes of the second fullyconnected layer respectively represent the five windows defined by thedisclosure.

A regression model for position estimation and a classification modelfor removing noise interference may be obtained by training the datasubsets by the CNN model. The step C comprises following steps C01 toC06.

In step C01, in order to reduce the impact of data correlation errorsand improve the speed of network convergence, the method in thedisclosure standardizes each indoor CSI data in each data subset:

$\begin{matrix}{{x_{p} = \frac{{var}_{p} - {{mean}( {var}_{p} )}}{{total}( {var}_{p} )}};} & (3)\end{matrix}$

where x_(p) represents the p^(th) indoor CSI data after beingstandardized, var_(p) represents the p^(th) indoor CSI data in the datasubset, mean ( ) is a function MEAN, total ( ) is a function SUBTOTAL,p=1, . . . , n, where n is the number of indoor CSI data in each datasubset.

In step C02, model parameters of the improved CNN model are initialized,the standardized indoor CSI data in each data subset are input into theconvolution layers of the improved CNN model to obtain the convolutedindoor CSI data, and features of the convoluted indoor CSI data areextracted.

The indoor CSI data in each data subset are input into the convolutionlayers in sequence, and the features of the tracking results (theconvoluted indoor CSI data) are extracted by a series of kernelfunctions. One kernel generates one tracking result feature mapping, andelements in the kernel are determined by network training. Each kernelis a three-dimensional matrix, with its rows and columns as user definedhyper-parameters.

In step C03, batching, max-pooling and activation are performed on thefeatures of the convoluted indoor CSI data to obtain mappings of theconvoluted indoor CSI data.

Batching can speed up network training, allow higher learning rates,make initial weighting easier, and simplify the creation of deepnetworks. The batching in the embodiment of the disclosure comprisesfollowing steps (1) to (3).

In step (1), the mean is calculated based on the features of theconvoluted indoor CSI data:

$\begin{matrix}{{U_{B} = {\frac{1}{M}\Sigma_{q = 1}^{M}s_{q}}};} & (4)\end{matrix}$

where U_(B) represents the mean of the features of the convoluted indoorCSI data, M is the number of features of each convoluted indoor CSIdata, s_(q) represents the features of the q^(th) convoluted indoor CSIdata, q=1, . . . , M.

In step (2), a variance of the features is calculated based on U_(B) byfollowing formula:

$\begin{matrix}{{\sigma_{B}^{2} = {\frac{1}{M}{\Sigma_{q = 1}^{M}( {s_{q} - U_{B}} )}^{2}}};} & (5)\end{matrix}$

where σ_(B) ² represents the variance of the features of the convolutedindoor CSI data.

In step (3), the features of the convoluted indoor CSI data arestandardized based on U_(B) and σ_(B) ²:

$\begin{matrix}{{= \frac{s_{q} - U_{B}}{\sqrt{\sigma_{B}^{2} + \varepsilon}}};} & (6)\end{matrix}$

where

represents the standardized features of the indoor CSI data, and ε is adefault standardized value.

Pooling is an important concept of convolution layer, which is in formof nonlinear down-sampling. According to the disclosure, the trackingresults are partitioned into a set of non-overlapping rectangles bymax-pooling, and a maximum value is output in each rectangle. On thepremise of keeping the main features, the computation burden is reducedby reducing the number of parameters.

According to the disclosure, the method performs activation by takingLeaky ReLU as an activation function, and the activation function canmake the CNN neural network show nonlinear features.

In step C04, the mappings of the convoluted indoor CSI data areclassified by the second fully connected layer of the improved CNN modelto obtain the coordinate information corresponding to each indoor CSIdata in each data subset.

Each fully connected layer is a column vector comprising a plurality ofnerve units, and coordinate information output thereby is calculated byfollowing formula:

y _(p)=γ

_(p)+β  (7);

where y_(p) represents the coordinate information corresponding to thep^(th) indoor CSI data in the data subset, γ and β are the modelparameters of the improved CNN model, respectively,

_(p) represents the standardized features of the p^(th) indoor CSI datain the data subset.

In step C05, based on a default real coordinate label (i.e., the real 3Dcoordinate information corresponding to the CSI data) and the coordinateinformation (y_(p)) output by the improved CNN model, losses of theimproved CNN model are calculated by a loss function by followingformula:

L=−Σ _(p) y′ _(p) log(y _(p))  (8);

where L represents the losses of the improved CNN model, y′_(p)represents the real coordinate label of the p^(th) indoor CSI data inthe data subset, and y_(p) represents the coordinate informationcorresponding to the p^(th) indoor CSI data in the data subset.

In step C06, the model parameters of the improved CNN model are updatedbased on the losses, and the standardized indoor CSI data are processedby the updated improved CNN model. The steps C02 to C06 are repeated,and the loss convergence is observed through a loss function diagram.When the losses converge, the trained improved CNN model is obtained.

According to the disclosure, in step D, a large number of indoor CSIdata are collected and processed by the trained improved CNN model, sothat the coordinate information corresponding to the CSI data can beobtained, and the positioning fingerprint database is generated by theCSI data and the corresponding coordinate information.

According to the disclosure, the obtained positioning fingerprintdatabase can be used directly for indoor positioning in the onlinestage, and the target CSI data of the to-be-positioned target arecollected by the SDR platform. In step 2, the target CSI data arecompared with the data in the positioning fingerprint database, and ifthe target CSI data are the same as the CSI data in the positioningfingerprint database, coordinate information can be directly extractedfrom the fingerprint database. In addition, considering that the targetCSI data may not be exactly the same as the CSI data in the positioningfingerprint database, the fingerprint matching in the disclosure mayalso be performed according to the steps shown in FIG. 3 ,multi-dimensional data operation and matching are performed based on thefeatures of a root node and a split point, and the coordinateinformation with the highest matching degree is selected.

According to the disclosure, the collected CSI data are processed by PCAand a data subset partitioning method, so as to remove noiseinterference and preliminarily improve the accuracy of positioning. TheCSI data in the data subset are put into the improved CNN model fortraining, so as to suppress multi-path components and further improvethe accuracy of indoor positioning. The method according to thedisclosure can effectively remove noise and multi-path components,improve the accuracy of positioning and realize its application value inlarge indoor places.

It will be obvious to those skilled in the art that changes andmodifications may be made, and therefore, the aim in the appended claimsis to cover all such changes and modifications.

What is claimed is:
 1. A method, comprising: acquiring and preprocessingtarget camera serial interface (CSI) data of a to-be-positioned target;and matching preprocessed target CSI data with fingerprints in apositioning fingerprint database to obtain coordinate information of theto-be-positioned target; wherein: a generation method of the positioningfingerprint database comprises: collecting indoor WiFi signals by asoftware defined radio (SDR) platform to obtain indoor CSI datacorresponding to the WiFi signals, and preprocessing the indoor CSIdata; partitioning the preprocessed indoor CSI data into a plurality ofdata subsets through a clustering algorithm; training an improvedconvolutional neural network (CNN) model by the data subsets to obtain atrained improved CNN model; and generating the positioning fingerprintdatabase by the trained improved CNN model and the preprocessed indoorCSI data.
 2. The method of claim 1, wherein the SDR platform comprises amobile node, a plurality of signal base stations and a data processingunit; the mobile node is configured to periodically broadcast proberequest frames and automatically send positioning requests; each signalbase station is configured to receive the positioning requests of themobile node and acquire WiFi signals of the mobile node; and the dataprocessing unit is configured to process the WiFi signals acquired bythe signal base stations to obtain the indoor CSI data corresponding tothe WiFi signals.
 3. The method of claim 1, wherein preprocessing targetCSI data comprises: performing amplitude filtering, phase correction,and mean removal on the indoor CSI data to obtain corrected CSI data;and reducing dimension of and eliminating noise of the corrected CSIdata by principal component analysis (PCA) to obtain the preprocessedCSI data.
 4. The method of claim 1, wherein partitioning thepreprocessed indoor CSI data into a plurality of data subsets through aclustering algorithm comprises: dividing the preprocessed indoor CSIdata into multiple data subsets by clustering algorithm, and each datasubset comprising multiple indoor CSI data; calculating a center of eachdata subset based on a k-means algorithm by following formula:$\begin{matrix}{{\mu_{i} = {\frac{1}{C_{i}}\Sigma_{x \in C_{i}}x}};} & (1)\end{matrix}$ where μ_(i) represents a center of an i^(th) data subset,C_(i) represents the i^(th) data subset, x represents the indoor CSIdata, i=1, . . . , k, where k is a number of data subsets; calculating asquare error of all data subsets based on the center of the data subsetby following formula:E=Σ _(i=1) ^(k)Σ_(x∈C) _(i) ∥x−μ _(i)∥₂ ²  (2); where E represents thesquare error of all data subsets; and calculating a distance between thepreprocessed indoor CSI data and the center of each data subset,re-partitioning the data subsets according to the distance until thesquare error is minimized, thereby obtaining a final data subset.
 5. Themethod of claim 1, wherein the improved CNN model comprises fiveconvolution layers, a first fully connected layer, and a second fullyconnected layer connected in sequence; the five convolution layerscomprise 16, 32, 64, 64 and 128 kernels, respectively; the first fullyconnected layer is configured to flatten outputs of a fifth convolutionlayer, and the second fully connected layer is configured to outputpartitioned CSI data.
 6. The method of claim 5, wherein training animproved CNN model by the data subsets comprises: standardizing eachindoor CSI data in each data subset: $\begin{matrix}{{x_{p} = \frac{{var}_{p} - {{mean}( {var}_{p} )}}{{total}( {var}_{p} )}};} & (3)\end{matrix}$ where x_(p) represents a p^(th) indoor CSI data afterbeing standardized, var_(p) represents a p^(th) indoor CSI data in thedata subset, mean ( ) is a function MEAN, total ( ) is a functionSUBTOTAL, p=1, . . . , n, where n is a number of indoor CSI data in eachdata subset; initializing model parameters of the improved CNN model,inputting the indoor CSI data standardized in each data subset into theconvolution layers of the improved CNN model to obtain the convolutedindoor CSI data, and extracting features of the convoluted indoor CSIdata; batching, max-pooling and activating the features of theconvoluted indoor CSI data to obtain mappings of the convoluted indoorCSI data; classifying the mappings of the convoluted indoor CSI data bythe second fully connected layer of the improved CNN model to obtaincoordinate information corresponding to each indoor CSI data in eachdata subset; calculating losses of the improved CNN model by a lossfunction based on a default real coordinate label and the coordinateinformation output by the improved CNN model; and updating the modelparameters of the improved CNN model based on the losses, andprocessing, by an updated improved CNN model, the indoor CSI datastandardized until the losses converge, to obtain the trained improvedCNN model.
 7. The method of claim 6, wherein batching the features ofeach convoluted indoor CSI data comprises: calculating a mean based onthe c data: $\begin{matrix}{{U_{B} = {\frac{1}{M}\Sigma_{q = 1}^{M}s_{q}}};} & (4)\end{matrix}$ where U_(B) represents the mean of the features of theconvoluted indoor CSI data, M is a number of features of each convolutedindoor CSI data, s_(q) represents the features of a q^(th) convolutedindoor CSI data, q=1, . . . , M; calculating a variance of the featuresbased on U_(B) by following formula; $\begin{matrix}{{\sigma_{B}^{2} = {\frac{1}{M}{\Sigma_{q = 1}^{M}( {s_{q} - U_{B}} )}^{2}}};} & (5)\end{matrix}$ where σ_(B) ² represents the variance of the features ofthe convoluted indoor CSI data; standardizing the features of theconvoluted indoor CSI data based on U_(B) and σ_(B) ²: $\begin{matrix}{{= \frac{s_{q} - U_{B}}{\sqrt{\sigma_{B}^{2} + \varepsilon}}};} & (6)\end{matrix}$ where

represents standardized features of the indoor CSI data, and ε is adefault standardized value.
 8. The method of claim 6, wherein thecoordinate information corresponding to each indoor CSI data in eachdata subset is calculated by following formula:y _(p)=γ

_(p)+β  (7); where y_(p) represents the coordinate informationcorresponding to the p^(th) indoor CSI data in the data subset, γ and βare the model parameters of the improved CNN model, respectively, and

_(p) represents the standardized features of the p^(th) indoor CSI datain the data subset.
 9. The method of claim 6, wherein the loss functionis expressed as follows:L=−Σ _(p) y′ _(p) log(y _(p))  (8); where L represents the losses of theimproved CNN model, y′_(p) represents a real coordinate label of thep^(th) indoor CSI data in the data subset, and y_(p) represents thecoordinate information corresponding to the p^(th) indoor CSI data inthe data subset.
 10. The method of claim 1, wherein the positioningfingerprint database comprises CSI data and coordinate informationcorresponding to the CSI data.